New Infosys AI tool could transform the way companies maintain complex systems

For years it has made a good living helping customers manage legacy tools, but CEO Vishal Sikka, who took over 21 months ago saw a shifting landscape and he began implementing new systems immediately.

One of those changes involved developing a new artificial intelligence system they have called Mana, which is designed to help customers automate repetitive system maintenance tasks and build knowledge about the underlying systems using artificial intelligence and machine learning. Sikka announced the system this morning at the Infosys Confluence conference in San Francisco.

Mana involves three main tools: Infosys Information Platform for analytics, Infosys Automation Platform for automating and continuously building knowledge about system maintenance and workflow tasks and Infosys Knowledge Platform, a formal platform for capturing and storing knowledge.

Infosys understands first-hand the huge amount of work and energy that goes into maintaining large legacy systems, whether computers or mechanical systems like turbines, locomotives and airplane engines.

The company has more than 11,000 people alone working on fixing software bugs, a time-consuming and tedious task, which involves not only identifying a bug and a fix, but also the dependencies that fix could affect (and likely break).

Oracle Artificial Intelligence

Artificial intelligence (AI) refers to the ability of computers to perform tasks usually associated with human intelligence.

Today&rsquo;s AI-enabled computers can recognize images, understand language, and perform complex reasoning while making decisions based on sophisticated mathematical analyses.

These three factors call for augmenting human capabilities with artificial intelligence in order for businesses to remain relevant in a quickly changing world.

As companies begin their enterprise AI journey, they should keep these key considerations in mind: Oracle is in a unique position to deliver AI across all cloud services, including embedded AI within business applications, AI services within Oracle&rsquo;s platform, and an AI development environment running on the high-performance compute and network infrastructure required for accelerated model training.

Oracle&rsquo;s cross-industry domain knowledge in many facets of IT operations and ten global, industry-specific, operational and analytical business processes are the foundation of our AI solutions.

Machine learning algorithms tell computer systems to sift through vast quantities of data over and over again until they can recognize patterns of information that indicate normal operations, as well as other patterns that indicate something out of the ordinary has happened.

These dynamic new AI-infused applications adapt and learn according to the data they process, are aimed at a wide range of business professionals, and provide them with the customer insights to make better decisions.

The first adaptive intelligent application is aimed at helping customer experience professionals run personalized marketing campaigns in the context of offers and actions that the system recommends for individual consumers.

It combines big-data processing techniques with machine learning to help companies detect anomalies in very large data sets that indicate problems on a manufacturing line, for example, or patterns that indicate system security has been breached.

The data includes the identities of people logging onto the system, for example, or the temperature and pressure readings on a manufacturing line gathered by sensors and transmitted over the internet.

Oracle embeds machine-learning capabilities into Oracle Database, where moving analytics closer to the data helps customers get fast, accurate answers to business questions and make fact-based predictions about the future.

Oracle Data Cloud offers some five billion profiles of consumers and companies and 45,000 attributes of the consumer lifestyle, giving companies a much deeper understanding of who their customers are, what they do, and what they buy.

Reshaping Business With Artificial Intelligence

Perhaps the most telling difference among the four maturity clusters is in their understanding of the critical interdependence between data and AI algorithms.

Compared to Passives, Pioneers are 12 times more likely to understand the process for training algorithms, 10 times more likely to understand the development costs of AI-based products and services, and 8 times more likely to understand the data that’s needed for training AI algorithms.

Most organizations represented in the survey have little understanding of the need to train AI algorithms on their data so they can recognize the sort of problem patterns that Airbus’s AI application revealed.

Citrine Informatics, a materials-aware AI platform helping to accelerate product development, uses data from both published experiments (which are biased toward successful experiments) and unpublished experiments (which include failed experiments) through a large network of relationships with research institutions.

knowing what does not work helps it to know where to explore next.3 Sophisticated algorithms can sometimes overcome limited data if its quality is high, but bad data is simply paralyzing.

Other data is fragmented across data sources, requiring consolidation and agreements with multiple other organizations in order to get more complete information for training AI systems.

The need to train AI algorithms with appropriate data has wide-ranging implications for the traditional make-versus-buy decision that companies typically face with new technology investments.

Training AI algorithms involves a variety of skills, including understanding how to build algorithms, how to collect and integrate the relevant data for training purposes, and how to supervise the training of the algorithm.

The chief information officer of a large pharma company describes the products and services that AI vendors provide as “very young children.” The AI tech suppliers “require us to give them tons of information to allow them to learn,” he says, reflecting his frustration.

We believe the juice is not worth the squeeze.” To be sure, for some support functions, such as IT management and payroll support, companies might choose to outsource the entire process (and pass along all of their data).

Even if companies expect to rely largely on external support, they need their own people who know how to structure the problem, handle the data, and stay aware of evolving opportunities.

“Five years ago, we would have leveraged labor arbitrage arrangements with large outsourcers to access lower cost human labor to do that work,” the pharma company CIO says.

Eric Horvitz, director of Microsoft Research, argues that the tech sector is quickly catching up with the new model of offering technology tools to use with proprietary data, or “providing industry with toolsets, computation, and storage that helps to democratize AI.” Many AI algorithms and tools are already in the public domain, including Google’s TensorFlow, GitHub, and application programming interfaces from tech vendors.

The data issues can be pronounced in heavily regulated industries such as insurance, which is shifting from a historic model based on risk pooling toward an approach that incorporates elements that predict specific risks.

But we use machine learning algorithms to assess the model’s non-linear construction, variables and features entered, and as a benchmark for how well the traditional model performs.” As technology races ahead of consumer expectations and preferences, companies and the public sector tread an increasingly thin line between their AI initiatives, privacy protections, and customer service.

Likewise, a technology vendor offers an AI-based service to help call center operators recognize when customers are getting frustrated, using real-time sentiment analysis of voice data.